Senior Data Platform Engineer

HRB; Toronto; On-site; CAD 100,000 - 130,000; Full time

Job summary

A technology company is seeking an experienced engineer to develop scalable data platforms. Responsibilities include enhancing data replication, optimizing pipelines, and managing infrastructure for high availability. The ideal candidate has over 6 years in data engineering, with strong skills in Java and real-time workflows. This role requires collaboration across teams and a strategic approach to architecture.

Qualifications

• 6+ years of experience in platform engineering or data engineering.
• 4+ years of experience designing and optimizing data pipelines at TB to PB scale.
• Proficient in Java with a focus on clean, maintainable code.
• Strong problem-solving skills and a collaborative mindset.

Responsibilities

• Own projects aimed at enhancing data replication, storage, and reporting capabilities.
• Build and optimize efficient streaming and batch data pipelines.
• Design scalable storage solutions for handling petabytes of IoT data.
• Develop and maintain real-time data systems.

Skills

Java

Python

Data engineering

System design

Real-time workflows

Big data processing

Collaboration

Tools

AWS S3

AWS Kinesis

Apache Kafka

Apache Flink

Apache Iceberg

Job description

About the role

We’re looking for an engineer who thrives on building scalable data platforms and enjoys tackling complex backend challenges. This isn’t just a data engineering role, you’ll be designing and optimizing the data platform that powers Terminal’s API, managing everything from data streaming and storage to analytics features at petabyte scale.

You will lead building the data platform for the long-term vision. You’ll not only design and deliver complex systems, but also drive technical strategy, set architectural direction, and influence how the data platform evolves. You’ll partner closely with product, engineering teams, and leadership to ensure we’re building the right abstractions and reusable components that scale with our growth. You know when to slow down to build the right solution vs. work against time. This is a role with broader ownership, where your leadership and judgment will raise the technical bar across the team and directly impact how customers succeed with high-volume telematics data.

What you will do:

• Own projects aimed at enhancing data replication, storage, enrichment, and reporting capabilities.
• Build and optimize efficient streaming and batch data pipelines that support our core product and API.
• Design scalable storage solutions for handling petabytes of IoT and time-series data.
• Develop and maintain real-time data systems to ingest growing data volumes.
• Implement distributed tracing, data lineage and observability patterns to improve monitoring and troubleshooting.
• Manage infrastructure that powers the current and future services, ensuring scalability, reliability, and zero downtime for HA services.
• Write clean, maintainable code in Java and Python for various platform components.
• Shape architectural decisions to ensure scalability and reliability throughout the data platform.

The ideal candidate will have:

• 6+ years of experience in platform engineering or data engineering.
• 4+ years of experience designing and optimizing data pipelines at TB to PB scale.
• Proficient in Java, with a focus on clean, maintainable code.
• Strong system design skills with a focus on big data and real-time workflows.
• Experience with lake-house architectures (e.g., Iceberg, Delta, Paimon).
• Experience with real-time data processing tools like Kafka, Flink and Spark.
• Knowledge of distributed systems and large-scale data challenges.
• Strong problem-solving skills and a collaborative mindset.
• Nice-to-have:
  • Experience working with orchestration / workflow engines (e.g. Step Functions, Temporal)
  • Experience with serverless and/or event-driven architectures (e.g. AWS Lambda, SQS).
  • Experience with Javascript/Typescript languages (for cross team work)

Tech Stack

• Languages:Java, Python
• Framework:Springboot
• Storage:AWS S3, AWS DynamoDB, Apache Iceberg, Redis
• Streaming:AWS Kinesis, Apache Kafka, Apache Flink
• ETL:AWS Glue, Apache Spark
• Serverless: AWS SQS, AWS EventBridge, AWS Lambda and Step Functions.
• Infrastructure as Code:AWS CDK
• CI/CD:GitHub Actions